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Abstract:

Previous patents and research have focused on the problem of determining
whether the quantitative EEG (QEEG) can discriminate a traumatic brain
injury (TBI) subject from a normal individual. The patents and research
have had varying degree of specificity in defining the variables involved
in obtaining a high degree of discriminant ability. However, all research
has limited its approach to the collection of eyes closed data and most
confine themselves to under 32 Hertz. The present patent employs 4
cognitive activation tasks, an eyes closed task, 19 locations, the high
frequency 32-64 Hz range, Spectral Correlation Coefficient (SCC) and
phase algorithms to obtain 100% correct identification in a group of over
195 subjects (normal and traumatic brain injured (TBI)) across the 4
cognitive activation tasks and eyes closed task and was successful in
correct identification of 50 participants randomly misclassified as
normal or brain injured across the five tasks (10 per task).

Claims:

1. The claims of this utility patent (which is useful and different from
previous patents in this area) are that by employing Lexicor's Spectral
Correlation Coefficients (SCC) and phase relations for the beta2 (32-64
Hz) frequency (between 19 locations of the 10-20 system) during the eyes
closed condition and four cognitive tasks (auditory and visual attention,
listening to stories, reading silently) and the relative power of beta2
at 6 frontal locations (Fp1, Fp2, F7, F8, F3, F4) a 100% correct
identification of the traumatic brain injured group and normal group with
no false positives or false negatives can be obtained in all the tasks.
In addition, 5 participants in each task were misclassified as either
normal (and was brain injured) or brain injured (and was normal) for a
total of 50 misclassifications across the 5 tasks. The discriminant
analysis was able to correctly identify the mistake in all 50
misclassifications. Thus, the results of this analysis indicates that any
cognitive task could be employed to obtain this result.

[0026] Previous utility patents addressing the discriminate ability of the
quantitative EEG to differentiate traumatic brain injury from normal
groups have typically relied upon one session of eyes closed data and a
frequency range of 0 to 32 Hertz. To assess communication patterns (SCC
and phase) the different hardware manufacturers and software engineers
have employed different mathematical algorithms to calculate these
values. The Lexicor SCC variable assesses the degree of similarity across
a period of time (epoch) while the phase variable measures the time delay
of a frequency between two locations.

[0027] A patent search in this area revealed patents which focus on this
general area without specifically attempting to differentiate TBI from
normal. These patents generally indicate that their approach will be able
to differentiate brain injured groups from normals without providing
specific information on exactly what variables will be employed. In
addition, the patents do not typically report discriminant results in
terms of false positives and false negatives.

[0028] The value of a patent which can quickly and reliably differentiate
between a TBI subject and a normal subject would be an important
contribution to the sports arena, emergency rooms, and returning Iraq and
Afghanistan war veterans, as TBI has been considered the signature wound
of these wars.

PREVIOUS PATENTS

[0029] The Suffin patent (U.S. Pat. No. 6,622,036 B1) addressed focused on
gathering QEEG data for "classifying, diagnosing and treating physiologic
brain imbalances". The patent's methodology is to compare a subject's
QEEG response to a clinically identified comparison group or normal group
to determine the brain's imbalances, examines the differences for
possible intervention decisions, and examine the QEEG response to
different medication interventions.

[0030] The QEEG variables under consideration were the absolute magnitudes
of the different frequencies (0 to 35 Hz), relative power, coherences,
peak frequency and symmetry measures. They list a number of clinical
conditions that they have in their database, including traumatic brain
injured. However, they did not discuss the parameters or statistical
method that would be involved in differentiating normal subjects from
traumatic brain injured subjects. They also did not report any
discriminant analysis results of TBI vs. normal.

[0031] The Williams patent (U.S. Pat. No. 6,796,941 B2) relates to "data
evaluation equipment and procedures for the monitoring and management of
brain injuries in mammals". The EEG is just one of the measures proposed.
The patent does not discuss specific EEG parameters which relate to brain
injury, but focused on seizure activity in terms of the EEG.

[0032] The Jordan patent (U.S. Pat. No. 6,985,769 B2) proposed a "method
and system for automated real time interpretation of brain waves in an
acute brain injury of a patient using correlations between brain wave
frequency power ratio and wave morphology, determine by a measure of the
rhythmicity and variability of the brain wave as a function of the slope
of the brain wave upstroke, the arc of the brain wave, and the
synchronicity of the brain wave". The authors propose that a power ratio
such as alpha-beta/theta-delta would be the useful variable. On the basis
of the data they have collected they have argued that the brain injury
results in an increase in the slower frequencies, compared to a normal
referenced group. They do not, however, discuss the issue of coherence or
phase nor provide discriminant analysis.

[0033] The Causevic patent (U.S. Pat. No. 7,720,530 B2) addresses a
field-deployable concussion detector. They propose using less than 10
electrodes and extending the frequency range to 50 Hz and even 1000 Hz.
The patent proposes to employ absolute power, relative power, symmetry
and coherence as the critical differentiating variables between normals
and the traumatic brain injured (TBI). However, they never provide what
specific variables are relevant to the TBI discriminant. Thus, the patent
is a method to discover what variables are relevant to the TBI situation.

[0034] The Cox patent (No. 2009/0156954 A1) addresses diagnosing
attentional impairment using EEG data and include the traumatic brain
injured subject as having as having attentional problems. The patent
mostly discusses the ADD/ADHD diagnosis issue in terms of elevation of
the lower frequencies (theta, in particular).

PREVIOUS RESEARCH ON DISCRIMINATING TBIS FROM NORMAL PARTICIPANTS WITH THE
QEEG

[0035] FIG. 1 presents the locations and nomenclature for the standard
10-20 system which is employed in the quantitative EEG field. The
research involved all locations.

[0037] Tabano, Cameroni and Gallozzi (1988) investigated posterior
activity of subjects (N=18) at 3 & 10 days following a MTBI and found an
increase in the mean power of the lower alpha range (8-10 Hz) and
reduction in fast alpha (10.5-13.5 Hz) with an accompanying shift of the
mean power of the lower alpha range (8-10 Hz) and reduction in fast alpha
(10.5-13.5 Hz) with an accompanying shift of the mean alpha frequency to
lower values. They also reported a reduction in fast beta (20.5-36 Hz)
activity. They did not conduct a discriminant analysis of TBI vs.
normals.

[0038] Thatcher, Walker, Gerson, & Geisler (1989) article was the first to
attempt to conduct a discriminant function analysis. They used the eyes
closed QEEG data to differentiate between 608 MTBI adult patients and 108
age-matched controls and obtained a discriminant accuracy rate of 90%.
Moderate to severe cases were not included in the analysis, nor was the
high frequency gamma band (32-64 Hz) or cognitive activation conditions.
The useful QEEG measures included increased frontal theta coherence
(Fp1-F3), decreased frontal beta (13-22 Hz) phase (Fp2-F4, F3-F4),
increased coherence beta (T3-T5, C3-P3), and reduced posterior relative
power alpha (P3, P4, T5, T6, O1, O2, T4). Three independent cross
validations (reported within the original research) resulted in accuracy
rates of 84%, 93%, and 90%.

[0039] Thatcher, Biver, McAlaster, Camacho and Salazar (1998) were able to
demonstrate a relationship between increased theta amplitudes and
increased white matter T2 Magnetic Resonance Imaging (MRI) relaxation
times (indicator of dysfunction) in a sample of mild TBI subjects.
Decreased alpha and beta amplitudes were associated with lengthened gray
matter T2 MRI relaxation times. The subjects were 10 days to 11 years
post injury. This study integrated MRI, QEEG (eyes closed) and
neuropsychological measures in a sample of MTBI subjects.

[0041] One review of the research in the traumatic brain injury area
indicated that numerous eyes-closed EEG and QEEG studies of severe head
injury (Glascow Coma Scale (GCS) score of 4-8) and moderate injury (GCS
score of 9-12) have agreed that increased theta and decreased alpha power
(microvolts) and/or decreased coherence and symmetry deviations from
normal groups often characterize such patients (J. R. Hughes & John,
1999).

[0042] The authors asserted that changes in these measures provide the
best predictors of long term outcome. The Thatcher discriminant function
(Thatcher et al., 1989) correctly identified 88% of the soldiers with a
blast injury history and 75% with no blast injury history (Trudeau et
al., 1998).

[0043] Other studies have reported that similar QEEG abnormalities are
correlated with the numbers of bouts or knockouts in boxers (Ross, Cole,
Thompson, & Kim, 1983) and with professional soccer players who
frequently used their heads to affect the soccer ball's trajectory
("headers"; Tysvaer, Storli, & Bachen, 1989). Neither of these research
reports attempted to develop a discriminant function analysis.

[0044] Barr et al. (2012) took EEG recordings from 5 frontal locations
(F7, Fp1, Fp2, F8 and a location below Fz) immediately post-concussion,
and 8 and 45 days after. They examined the frequency range up to 45 Hertz
on measures of absolute power, relative power, mean frequency, coherence,
symmetry and a fractal measure. Using a brain injury algorithm, abnormal
features of brain electrical activity were detected in athletes with
concussion at the time of injury which persisted beyond the point of
recovery on clinical measures.

[0045] Features that contributed most to the discriminant applied in this
study included: relative power increase in slow waves (delta and theta
frequency bands) in frontal, relative power decreases in alpha 1 and
alpha 2 in frontal regions, power asymmetries in theta and total power
between lateral and midline frontal regions, incoherence in slow waves
between fronto-polar regions, decrease in mean frequency of the total
spectrum composited across frontal regions and abnormalities in other
measures of connectivity (including mutual information and entropy). A
resulting discriminant score was employed to distinguish between the TBI
and normal group. If the discriminant score was above 65 there was a 95%
probability that the individual had experienced a TBI. The average
discriminant score change from the immediate post-concussion score of 75
to a score of 55 some 45 days later, thus rendering its ability to
discriminant after the original concussion not as useful as would be
desired.

[0046] The TBI's cognitive status, as assessed with neuropsychological
measures, had returned to the "normal" range at day 45, although brain
abnormalities were still present (TBI sample size=59). The researchers
did not internally attempt to replicate the findings within the sample
that they had obtained.

[0047] Previous research by Thornton (1997, 1999, 2000) focused on the
damage to the Spectral Coherence Correlation Coefficients (SCC--based
upon the Lexicor algorithms) and phase values in the beta2 (gamma; 32-64
Hertz) range when comparing the traumatic brain injured subject to the
normal group during eyes closed and different cognitive activation tasks.
The TBI sample size ranged from 22 to 32 with 52 normals (total N=84) in
the 1999 & 2000 studies. The present analysis employs around 197
participants. Lexicor Medical Technology (Boulder, Colo.) company
developed their own algorithms for coherence and phase. The coherence
measure algorithms were not the same as employed in the Barr et al.
(2012) study.

[0048] The Thornton results (1997, 1999, 2000) did not indicate any
deficits in the amplitudes or relative power of delta, theta or alpha. In
the Thornton (2003) article (addressing auditory memory) the alpha level
was set to 0.02 due to high number of significant findings in the beta2
SCC and phase values predominantly in the values involving the frontal
lobe. The TBI group showed lower beta2 coherence (SCC) values. The
article studied the relations between the QEEG variables and memory
performance in 85 TBI patients and 56 normal subjects.

[0049] The claim of this patent is to that it is possible obtain 100%
discriminant accuracy across 5 cognitive tasks. Confirmatory evidence is
obtained by employing a misclassification (of both normal and brain
injured participants) approach and testing the ability of the
discriminant analysis to correctly identify the misclassification across
the 5 tasks. The discriminant analysis was successful in 100% of the 50
misclassifications involving the cognitive and eyes closed tasks.

[0050] Almost all of the previous research has not examined the beta2
frequency in terms of absolute, relative power or phase and coherence
(SCC) relations. The data available to the author was reexamined for
potentially useful variables. The standard eyes closed task collects data
for 300 seconds. The Auditory Attention task requires the eyes closed
subject to place their hand on their right knee and raise their index
finger whenever they hear the sound of the pen tapping on a table.

[0051] The Visual Attention task has the subject look at a laminated sheet
of upside down Spanish text. Similar to the Auditory Attention task, the
subject has their right hand on their right leg. When they see the flash
of a laser light on the sheet of paper they are to raise their index
finger. Each of the attention tasks last 200 seconds each. The reading
task requires the subject to silently read a story presented on a
laminated sheet of paper for 100 seconds. Thus the evaluation requires,
at present, 800 seconds or 13.3 minutes. Reliability data for QEEG data
typically is in the 0.90 to 0.95 range.

[0052] The discriminant analysis employed all 19 locations (FIG. 1) and
the SCC and phase values (32-64 Hz) of all the interrelations between
these 19 locations and the relative power of beta2 (32-64 Hz) from 6
frontal locations (Fp1, Fp2, F7, F8, F3, F4). FIG. 2 presents the
relations which were significantly below the normative reference group
(alpha set to 0.05) for the SCC and phase values. The lines connecting
the locations indicate a significant deficit in the relations between the
two locations.

FIG. 2--Significant SCC and phase deficits in the TBI participant

Insert FIG. 2

CB2=Coherence (SCC) beta2: PB2=Phase beta2

[0053] The following tables present the discriminant function (General
Discrimination analysis Model employed in CSS Statistica--vs. 8) results
for the different tasks. All of the tables indicated 100% accuracy in
discriminating normal from brain injury. The time between the date of the
head injury and evaluation ranged from 12 days to 30 years. The average
age of the total sample (listening task data) was 39.47 with a range
between 14.08 years to 72.42 years. There were 95 males and 102 females
in the listening task group (total N=197). There were 88 participants
classified as TBI and 109 participants classified as normal. There was a
range of 162-197 subjects involved in the different conditions. Tables
1-5 present the resulting discriminant analysis for the five tasks. As
the tables indicate the discriminant analysis were 100% effective in
distinguishing between the TBI and normal participants.

[0054] To determine if the discriminant algorithm could accurately
indicate a misclassification, five TBI subjects and five normal subjects
were misclassified (for each task) as to their status and the
discriminant analysis was recalculated to determine if the inaccurate
classification was identified. Ten different subjects were selected for
each task for a total of 50 misclassifications.

[0055] Row 1 indicates the # of errors in the initial discriminant
analysis. The 0 number indicates no misclassifications. Row 2 indicates
how the group of 5 participants were misclassified. The label MC as TBI
indicates that 5 normal participants were misclassified as TBI. The MC as
N label indicates that 5 TBI participants were misclassified as normal.
Row 3 indicates the number of errors resulting for the group in the b
column. For the 5 TBI participants misclassified as normal the reanalysis
indicated the misclassification and thus 0 errors. Row 4 indicates the
error rate for the normal participants who were misclassified as TBI. Row
5 indicates the overall error rate across both methods. As the table
indicates there were no errors in any of the approaches.

[0056] The problem of determining if a person in a sports event has
experienced a concussion presents two additional difficulties. The first
is whether the initial post concussive brain state is going to be
significantly different that the concussed brain state some 12 days to 30
years later. The second is that a previous concussion could be affecting
the results. It is therefore possible that the discriminant approach will
be identifying the previous concussion and there is not a concussive
event presently.

[0057] Evidence towards the first problem is provided in the Thornton
(2000) and Thatcher (1998) articles. These studies indicate that the EEG
concussed pattern does not change over time. Thus, the concussed brain
injury QEEG signature should be evident at the time of the initial
concussion.

[0058] In addition, the work of Barr et al. (2012) indicates that the
TBI's brain pattern remains affected despite improvement in cognitive
function, thus indicating a compensation response, i.e. the brain employs
other resources to accomplish the cognitive task. The compensated QEEG
response pattern was also documented in the Thornton (2003) article and
book chapter (Thornton & Carmody, 2009) showing a right hemisphere
compensation approach.

[0059] To address the second problem, the work of Slobounov, Cao &
Sebastianelli (1990) addressed the problem of the second concussion. The
researchers employed a wavelet entropy (EEG-IQ) algorithm. The algorithm
addresses the Information Quality of EEG (EEG-IQ1) which first applies
discrete wavelet transform (DWT) to the EEG signal and then calculates
the traditional Shannon Entropy of the wavelet coefficients. This measure
was reduced at temporal, parietal and occipital locations after the first
concussion and particularly after the second.

[0060] In addition, Slobounov et al. (1990) reported that the EEG-IQ
measure was affected more after the second concussion compared to the
first concussion. Thus, the second concussion showed a similar EEG
effect. The QEEG pattern was also slower to recover. In addition, the
shorter the time interval between the two concussions resulted in larger
reductions of EEG-IQ values. The results also indicated a better outcome
after the first compared to the second concussion. Thus the authors were
able to show that a similar effect occurred during the second concussion
and it was more pronounced. Therefore, it is logical to assume a similar
effect would occur for the variables that this patent employs.

[0061] On the procedural level, the hypothetically concussed individual's
data could be entered into the Statistica spreadsheet or equivalent
software (containing the presently available data) and the five
discriminants run to determine if the subject had experienced a
concussion during an athletic event or other trauma. This approach
assumes there is no previous concussion. All five tasks or combination
could be administered to ensure accuracy. If a set of data on one task is
contaminated by artifact it wouldn't be employed for the decision. As all
the tasks have a high degree of discriminability there would be no loss
of discriminative power.

[0062] To address the problem of a previous concussion the following
methodology could be employed. A baseline functioning on the five tasks
could be obtained. As the QEEG variables are not subject to conscious
manipulation, the probability that the athlete would feign a bad baseline
response so that the on-field evaluation would be employing an "impaired"
baseline would be eliminated. The baseline evaluation would collect
cognitive performance as well as the QEEG data. The procedure could be
employed to determine if the participant has a brain injury pattern from
a previous concussion, employing the algorithms developed in the initial
research reported here. The main value of the baseline evaluation is to
provide data for comparison to the evaluation which takes place during
the subsequent athletic event.

[0063] To determine the presence of concussion during a sports event the
participant would undergo the same evaluation as occurred in the baseline
evaluation for the comparison analysis and the following analysis
conducted to determine if a new brain injury has occurred.

[0064] Eighty-six of the 171 coherence variables showed a significant
decrease (alpha @ 0.05) for an average change of 0.47 SD in the direction
of impaired QEEG variables. Seventy-seven of the 171 phase beta2
variables showed a significant difference from the normative group with
the average SD difference of 0.44. However, many of the variables were
close to the 0.05 level. The, coherence and phase beta2 variables and
frontal relative power of beta2 values indicated by the initial research
would be employed in the analysis. A 0.44 SD (coherence values) and 0.47
SD (phase values) change value would be employed as the cut off values to
render the decision. In the interest of being conservative the average SD
change could be lowered to 0.40 for both coherence and phase values. The
relative power of beta2 values for the TBI group were 0.47 SD above the
normative group. Thus employment of a 0.40 SD average for the 6 frontal
locations would be a conservative value for the cutoff. The medical
personnel involved in the decision could employ the logic and data of
this research as well as the knowledge of the event to determine the
presence of a brain injury.

[0065] If the discriminants indicated a concussion, then the player would
be taken out of the game and his progress assessed in the days/weeks
following the concussion. An EEG biofeedback program could be initiated
to address the QEEG problems. As previous research by Thornton & Carmody
(2000, 2002, 2005, 2008, 2009a, 2009b) has indicated that the QEEG
variables and cognition (auditory memory) can be improved. The TBI group
was performing better than the control group after the treatment. Thus,
it would be possible to "repair" the damage and enable the player to be
returned to play. This approach would have an advantage over previous
cognitive assessment methods which don't assess the physical parameters
of brain functioning but the brain's cognitive compensation.

BRIEF SUMMARY

[0066] The claims of this patent are that by employing the Spectral
Correlation Coefficients (SCC) and phase relations across different
cognitive tasks (eyes closed, auditory and visual attention, auditory
memory and reading silently) and 6 frontal relative power of beta2 values
a 100% correct identification of the traumatic brain injured group and
normal group with no false positives or false negatives can be obtained.
As the claim indicates that the results can be obtained across of number
of cognitive tasks, the claim is applicable to any cognitive task which
could be employed.

[0067] Thus the method can be useful to the medical personnel involved in
rendering the immediate diagnosis of a TBI (as in sports events). The
claim of this patent is particularly relevant to a) the sports concussion
area; b) emergency room diagnosis of the traumatic brain injury (TBI)
patient, as approximately 56% of TBIs are missed in the emergency room
(Powell et al., 2008) by present diagnostic approaches (rating scales,
behavioral observations); c) soldiers in combat situations; d) and
returning military veterans who may have experienced a TBI during combat.